Why AutoML Is Not Enough for Scaling AI

EvoML: Scalable End-to-end Data Science Lifecycle

TurinTech
TurinTech
Mar 1 · 9 min read

1. Introduction:

The practice of building AI is rapidly becoming automated. Automated Machine Learning (AutoML) is the process of automating the time consuming, iterative tasks of machine learning model development. However, AutoML on its own cannot bring AI to life in the real business world. In this article, we will give an overview of AutoML’s limitations, and explain how EvoML overcomes these limitations to make AI scalable.

2. What is AutoML?

Integrating AI into your business activities is a long-term process which requires specialised talent, a considerable amount of effort and investments. It requires well-sought-after data science talents who mainly tech giants can afford. Even in 2021, hiring and keeping data science experts is very difficult. In addition, it is still time-consuming and expensive to create AI models even when organisations have the talent they need.

Figure 1: Traditional ML VS AutoML. Source: https://joshjanzen.com/ml-vs-automl/
  1. Date pre-processing, Feature Engineering and Feature selection
  2. Model training and Hyperparameter optimisation
  3. Model Evaluation and Interpretation

3. Scaling AI Challenges that AutoML doesn’t tackle

AutoML accelerates data science lifecycle and makes AI more accessible. But AutoML mainly focuses on creating and perfecting model accuracy. There are some challenges to overcome to make AI scalable.

3.1 Efficiency: ML code needs to work well in production environment

There is a misconception that model accuracy is everything. As we have emphasised in our previous blog, AI is an iterative optimisation process to achieve multiple objectives, and efficiency is critical for scaling AI.

  • Maximum computing resource (cost) that your AI model can consume
  • Automatic retraining of the model when data patterns change
Figure 2 ML code is only a small fraction of the whole ecosystem. Image by Marcin Laskowski

3.2 Trust: Bias, Explainability and ML Code

AI is used across different sectors (healthcare, finance, criminal justice etc) and can make significant decisions that impact our lives. However, AI is intrinsically biased. Therefore, it is paramount that we understand and find ways to reduce bias and establish trust.

Figure 3 Black box AutoML. Image by MIT

3.3 Flexibility: Boxed solution without ML Code is Not Practical

Building sophisticated models requires flexibility. Most AutoML tools are boxed solutions, making it easy to build general AI solutions for business analysts. However, organisations have complex business problems that require sophisticated models. Data scientists are still required in this case to improve the model manually to meet their specific needs. When it comes to data scientists, the flexibility to use different AutoML approaches is a necessity. A boxed AutoML process which limits data scientists’ accessibility to the process, will hinder their creativity and productivity.

EvoML: Scalable End-to-end Data Science Lifecycle

Although AutoML significantly reduces the time it takes to build an ML model and improves its accuracy, it leaves out important attributes in the process necessary for any business to scale AI. To tackle the challenges of scaling AI mentioned above (efficiency, trust and flexibility), a more explainable and modular automation process is necessary, and ML code optimisation is the silver bullet.

Figure 4 EvoML: Scalable end-to-end data science lifecycle. Image by author.

About TurinTech

TurinTech is a research-driven deep tech company founded in 2018 based in London. TurinTech provides a platform for users with different level of skills to automatically build, optimise and deploy scalable AI within days.

Scaling AI with TurinTech

Making AI Scalable. https://turintech.ai

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